Article Per Year (5 Year)
p-Index From 2021 - 2026
7.821
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Teknotan: Jurnal Industri Teknologi Pertanian Bulletin of Electrical Engineering and Informatics AGRIEKONOMIKA Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Jurnal Optimasi Sistem Industri Kultivasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SAINS TANAH - Journal of Soil Science and Agroclimatology Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Agrotek Tropika Journal of Tropical Crop Science Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Community Services Cel Gunung Djati Conference Series Jurnal Pengabdian Masyarakat Nasional Journal Collabits Biodiversitas Journal of Biological Diversity
Rahmat Budiarto
Department Of Agronomy, Faculty Of Agriculture, Universitas Padjadjaran. Jl. Raya Bandung-Sumedang Km. 21, Jatinangor, Sumedang 45363, West Java, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

Published : 58 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 58 Documents
Search

 2   3   4   5   6 
Machine learning-based anomaly detection for smart home networks under adversarial attack Juli Rejito; Deris Stiawan; Ahmed Alshaflut; Rahmat Budiarto
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p122-129

Abstract

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.
Detection of Android malware with deep learning method using convolutional neural network model Reza Maulana; Deris Stiawan; Rahmat Budiarto
Computer Science and Information Technologies Vol 6, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i1.p68-79

Abstract

Android malware is an application that targets Android devices to steal crucial data, including money or confidential information from Android users. Recent years have seen a surge in research on Android malware, as its types continue to evolve, and cybersecurity requires periodic improvements. This research focuses on detecting Android malware attack patterns using deep learning and convolutional neural network (CNN) models, which classify and detect malware attack patterns on Android devices into two categories: malware and non-malware. This research contributes to understanding how effective the CNN models are by comparing the ratio of data used with several epochs. We effectively use CNN models to detect malware attack patterns. The results show that the deep learning method with the CNN model can manage unstructured data. The research results indicate that the CNN model demonstrates a minimal error rate during evaluation. The comparison of accuracy, precision, recall, F1 Score, and area under the curve (AUC) values demonstrates the recognition of malware attack patterns, reaching an average of 92% accuracy in data testing. This provides a holistic understanding of the model's performance and its practical utility in detecting Android malware.
Analyzing Supply Chain Risks Using the House of Risk Method: Evidence from the Salt Industry in Padang City Ahmad Syafruddin Indrapriyatna; Tessa Zulenia Fitri; Rahmat Budiarto
Jurnal Optimasi Sistem Industri Vol. 25 No. 1 (2026): Published in June 2026
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/josi.v25.n1.p120-132.2026

Abstract

Salt remains an important commodity for households and food-related businesses in Indonesia. In Padang City, the availability of salt can be affected when problems occur in raw material procurement, processing, or distribution. Such problems may also make daily activities more difficult for the actors in the chain. Although these risks are often encountered in practice, their priority in the local salt industry has not been systematically studied. This study applies the House of Risk (HOR) method to analyze supply chain risks in the salt industry of Padang City. The data were collected using questionnaires distributed to actors involved in procurement, processing, and distribution. These respondents were included because they handle the activities directly and understand the problems that appear in day-to-day operations. The analysis considers the severity of risk events, the occurrence of risk agents, and the relationship between risk events and their causes. These values were then used to calculate the Aggregate Risk Potential (ARP), which served as the basis for prioritizing risks. The findings show that risk exposure is mainly concentrated in procurement and production activities. This means that mitigation efforts should give more attention to these two stages. Preventive actions were then compared by looking at two practical considerations: how useful each action was expected to be and how difficult it would be to apply. The analysis shows that a small number of risk agents make a major contribution to the overall risk exposure. For this reason, these agents should be handled first. This order of action can help the actors reduce the most important risks without making the supply chain too rigid when conditions change.
Restoring subsoil degradation with mixed fertilizer-conditioner: A case study on red chili pepper (Capsicum annuum) cultivation Stefina Liana Sari; Nguyen Quoc Khuong; Emma Trinurani Sofyan; Saefur Rohman; Rahmat Budiarto; Eso Solihin
SAINS TANAH - Journal of Soil Science and Agroclimatology Vol 22, No 2 (2025): December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/stjssa.v22i2.97637

Abstract

The loss of topsoil in high-rainfall regions significantly reduces agricultural productivity, especially in degraded soils. This study investigated the effects of Mixed Fertilizer-Conditioner (MFC) on improving the chemical properties of subsoil cultivated with red chili peppers. A Randomized Block Design (RBD) with 11 treatments on subsoil and one control on normal soil was implemented, with three replications. The treatments included: A= subsoil without fertilizer, B= 0% MFC + full NPK, C= 25% MFC + full NPK, D= 50% MFC + full NPK, E= 75% MFC + full NPK, F= 100% MFC + full NPK, G= 50% MFC + 75% NPK, H= 50% MFC + 50% NPK, I= 50% MFC + 25% NPK, J= 50% MFC without NPK, and K= Full NPK on normal soil. The application of 100% MFC combined with full NPK significantly enhanced subsoil chemical properties. Soil organic carbon increased to 1.32%, pH rose to 6.3, CEC reached 22.1 cmol kg⁻¹, and base saturation improved to 49.4%. Nutrient availability also increased, including total N (1.21%), P (0.132%), K (0.677 cmol kg⁻¹), along with Ca (1362.72 ppm), Mg (311.04 ppm), and S (36.01 ppm). Micronutrients B, Co, and Zn also rose to 4.41 ppm, 18.95 ppm, and 11.97 ppm, respectively. Chili yields in subsoil treated with 50–100% MFC and full NPK exceeded 10 tons ha⁻¹. These results highlight the agronomic potential of MFC for rehabilitating degraded soils and recommend its use as a sustainable strategy to enhance soil fertility in low-fertility or erosion-prone areas, with implications for both farmers and agricultural policymakers.
Chemical properties analysis of liquid and semi-solid bioconversion products from organic waste and their effects on soil fertility and sweet corn yield Emma Trinurani Sofyan; Stefina Liana Sari; Saefur Rohman; Indra Permana; Rahmat Budiarto; Mansour Ghorbanpour; Sastrika Anindita
SAINS TANAH - Journal of Soil Science and Agroclimatology Vol 22, No 1 (2025): June
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/stjssa.v22i1.95664

Abstract

Food security remains a critical global challenge, particularly as land degradation, driven by excessive use of synthetic fertilizers, continues to threaten soil fertility and crop productivity. This study aimed to evaluate the characteristics of liquid and semi-solid fermented organic waste and their effects on several soil chemical properties and sweet corn yield. The experiment was conducted in a corn field in Pagerwangi Village, West Java, Indonesia. The experiment used a Split-Plot Design with three replications. The main plot was the fermented waste product treatment, which consisted of three levels: no product (A0), liquid product (A1), and semi-solid product (A2). The subplot was the N-P-K dose level, which consisted of four levels: 0 N-P-K (a0), 1/2 N-P-K dose (a1), 3/4 N-P-K dose (a2), and standard N-P-K dose (a3). The research findings indicated that the macro and microelements present in semi-solid products were several times higher compared to liquid ones. Furthermore, the microbial population in semi-solid products exhibited higher density compared to liquid products. Field tests also demonstrated that both liquid product (A1) and semi-solid product (A2) significantly increased total nitrogen, organic-C, and soil pH compared to the control (A0). The highest sweet corn productivity was observed in treatment A2, with a yield increase of 47.62% compared to the control. The research results suggested that the use of fermented organic waste products could enhance soil fertility and sweet corn production.
Deep Learning-Based Autism Detection Using Facial Images and EfficientNet-B3 Hasanudin, Muhaimin; Afiyati, Afiyati; Budiarto, Rahmat; Wahab, Abdi; Jokonowo, Bambang; Indrianto, Indrianto; Yosrita, Efy; Hanifah, Nurul Afif
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4574

Abstract

This study presents a novel deep learning approach for early detection of Autism Spectrum Disorder (ASD) using facial image analysis. Leveraging the EfficientNet-B3 model, the research addresses limitations in traditional diagnostic methods by autonomously extracting discriminative facial features associated with ASD. A balanced dataset of 2,940 facial images (1,470 autistic and 1,470 non-autistic children) from Kaggle was pre-processed to 200x200 pixels and evaluated under three dataset-splitting scenarios (80:10:10, 70:15:15, and 60:20:20) to assess generalisability. The model, trained with the Adam optimiser over 10 epochs, achieved optimal performance in the 80:10:10 scenario, with 84.67% precision, 84.35% recall, and 84.32% F1 score. Results demonstrate high confidence (>90% probability) in distinguishing autistic from non-autistic individuals on unseen data. The study underscores the potential of integrating deep learning into clinical decision-support systems for ASD detection, offering a robust, scalable, and efficient solution to improve diagnostic accuracy and reduce reliance on manual methods.
Melon Cultivation Guidance for Empowering Women in Pajagan Village, Sumedang Regency Rahmat Budiarto; Wawan Sutari; Farida; Mochamad Arief Soleh; Anne Nuraini; Syariful Mubarok; Kusumiyati; Siska Rasiska; Noor Istifadah; Luciana Djaya
Indonesian Journal of Community Services Cel Vol. 4 No. 2 (2025): Indonesian Journal of Community Services Cel
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70110/ijcsc.v4i2.96

Abstract

Background: As one of popular fruit, melon is potentially to cultivate in homeyard by housewives.Aims: This community service is carried out in July 2025, for empowering women in Pajagan Village, Cisitu District, Sumedang Regency through melon cultivation guidance.Method: Thirty-five participants joined, mostly local women housewives aged 25–50 from the PKK organization, along with 15 students aged 20–22 conducting fieldwork. This work documents the initial stages of home melon cultivation through a participatory approach and provides hands-on experience in melon seedling cultivation.Results: Participants’ enthusiasm and confidence in applying the seeding techniques learned reflect the effectiveness and practicality of the training methods in supporting home-based melon cultivation. This work is hoped to empowers women in managing home gardens, contributing to both economic resilience and household food security.
Implementation of Banana Cultivation and Post-Harvest Technology in Wanasari Village, Purwakarta Syariful Mubarok; Rahmat Budiarto; Fathi Rufaidah; Pipit Mutiara; Erni Suminar; Yanyan Mochamad Yani
Indonesian Journal of Community Services Cel Vol. 5 No. 1 (2026): Indonesian Journal of Community Services Cel
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70110/ijcsc.v5i1.131

Abstract

Background: Bananas are among the most important horticultural commodities in Indonesia. In West Java, Purwakarta is a center of banana production. Farmers rely on traditional propagation and production methods, resulting in low yields and productivity. The main problem for banana farmers is the lack of seed availability, where almost no farmer used seed from tissue culture and only 10% of the respondent did not know about post-harvest processing technology.Aims: The aim of this activity is to improve the skills of farmers in the propagation of banana plants by in vitro culture, banana production, and post-harvest technology. This activity took place in Wanasari, Purwakarta City.Methods: The methodology for these activities was to deliver lectures and conduct practice sessions with farmers on banana propagation, production, and postharvest technology. Additionally, we provided them with a banana plant from in vitro culture.Result: The community service activity showed that the farmers were highly interested and enthusiastic about the technology introduced to them with increasing the post-test score of those audiences. The participants' enthusiasm and confidence in implementing banana cultivation and post-harvest techniques demonstrate the effectiveness and practicality of the training methods in supporting banana cultivation. This work is expected to empower farmers to manage their gardens and post-harvest banana handling, thereby contributing to economic resilience and food security.
Morphological evaluation and determination keys of 21 citrus genotypes at seedling stage Rahmat Budiarto; Roedhy Poerwanto; Edi Santosa; Darda Efendi
Biodiversitas Journal of Biological Diversity Vol. 22 No. 3 (2021)
Publisher : Society for Indonesian Biodiversity

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/biodiv/d220364

Abstract

Abstract. Budiarto R, Poerwanto R, Santosa E, Efendi D. 2021. Morphological evaluation and determination keys of 21 citrus genotypes at seedling stage. Biodiversitas 22: 1570-1579. The identification of citrus varieties is generally based on flower, fruit, and mature tree characters. The detailed and comprehensive identification of seedling stage is very limited, therefore present study aimed to identify and distinguish 21 citrus genotypes based on 50 morphological characters of vegetative shoot at seedling stage. Cluster analysis using complete linkage agglomerative method showed broader dissimilarities between C. x limon and C. x microcarpa. Unfortunately, this method was limited to differentiate six genotypes within Citrus reticulata Blanco due to extremely low dissimilarities found. All citrus seedlings have similarities in the forms of habitus, gland spots, arrangement and venation of leaf. The result of PCA determined petiole wing, spine, color, hair and fragrance of leaves as five morphological markers at seedling stage. In addition, there was a positive correlation between spine and leaf pleasant. Moreover, the details of morphological dissimilarities between genotypes were described in arranged determination keys.
Short Communication: Allometric model to estimate bifoliate leaf area and weight of kaffir lime (Citrus hystrix) Rahmat Budiarto; Roedhy Poerwanto; Edi Santosa; Darda Efendi; Andria Agusta
Biodiversitas Journal of Biological Diversity Vol. 22 No. 5 (2021)
Publisher : Society for Indonesian Biodiversity

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/biodiv/d220545

Abstract

Abstract. Budiarto R, Poerwanto R, Santosa E, Efendi D, Agusta A. 2021. Short Communication: Allometric model to estimate bifoliate leaf area and weight of kaffir lime (Citrus hystrix). Biodiversitas 22: 2815-2820. Leaf is an economically important plant organ harvested from kaffir lime (Citrus hystrix DC.) for flavor and fragrance. This study aimed to formulate and validate regression models to estimate the leaf area (LA) and leaf weight (LW) of bifoliate kaffir lime leaf. There were 220 bifoliate leaves collected from 22 individual plants planted on Bogor. Bifoliate C. hystrix leaf consisted of upper main leaflets and winged petiole as lower secondary leaflet. All leaves were pooled and then grouped randomly into two subgroups for model formulation and validation, respectively. Linear, zero intercepts linear, exponential, logarithmic, polynomial, zero intercept polynomial and power regressions were used to properly estimate LA and LW. There was 63 formula obtained from nine predictors following seven regression models. Selected nine formulas with the highest R2 plus a stepwise formula were further tested for validation. Stepwise always showed the highest R2 followed by total of leaf ,length (TL) both on LA and LW estimation. However, the stepwise seemed to be more complicated and time wasted than TL regression model. Thus, our recommendation models for non-destructive and simple estimation in C. hystrix bifoliate leaf were LA = 0.1997 (TL)2 + 0.4571 (TL) and LW = 0.0067 (TL)2 + 0.0065 (TL), respectively.
Co-Authors Abdi Wahab Abdullakasim, Supatida Adi Hermansyah, Adi Aditya Pradana Afiyati, Afiyati Ahmad Heryanto, Ahmad Ahmed Alshaflut Al Aufa, Elfa Muhammad Ihsan Ali Firdaus Andria Agusta ANDRIA AGUSTA ANNE NURAINI ANNE NURAINI Anne Nuraini Anne Nuraini Anni Yuniarti Audrey, Berby Febriana Azka Ghafara Putra Agung Bambang Jokonowo BAYU PRADANA NUR RAHMAT Bayu Pradana Nur Rahmat Bedine Kerim, Bedine Bin Idris, Mohd Yazid Darda Efendi Darda Efendi Deris Stiawan Devita Aprilia Dikdik Kurnia Dwi Budi Santoso Dwinanda, Syahvan Rifqi Eddy Renaldi Edi Santosa Edi Santosa Edi Santosa Efendi, Darda Efy Yosrita, Efy Emma Trinurani Sofyan Emma Trinurani Sofyan Endo, Kenji Envry Artanti Duidahayu Putri Erik Setiawan Ermatita - ERNI SUMINAR Erni Suminar Erni Suminar Eso Solihin Eso Solihin Ezura, Hiroshi Fadlan Atalla Muhammad Fajri, Hauzan Ariq Musyaffa Fakhrurroja, Hanif Farida Farida Farida Farida Fathi Rufaidah Fauziah, Rossita Fiky Yulianto FIKY YULIANTO WICAKSONO Fiky Yulianto Wicaksono Firnando, Rici Firstina Iswari Ghorbanpour, Mansour Giyarto, Gunes Hanifah, Nurul Afif Haryanto, Yoyon Hauzan Ariq Musyaffa Fajri HAYANE ADELINE WARGANEGARA Hayane Adeline Warganegara, Hayane Adeline HELVI YANFIKA Helvi Yanfika HIROSHI EZURA Idris, Mohd Yazid Bin Iman Saladin B. Azhar INDAH LISTIANA Indah Listiana Indra Permana Indrapriyatna, Ahmad Syafruddin Indrianto Indrianto Iswari, Firstina Iwan Pahendra Jajang Sauman Hamdani Jatmika, Muhammad O. Juli Rejito Kemahyanto Exaudi Komala, Mega Kus Hendarto, Kus Kusumadewi, Vira Kusumiyati Kusumiyati Kusumiyati Luciana Djaya Luciana Djaya, Luciana M. Miftakul Amin Mansour Ghorbanpour Maolana, Adrian Mochamad Arief Soleh Mochamad Arief Soleh Mohamed Shenify Mohd Yazid Idris Mohd Yazid Idris Mohd. Yazid Idris Mugianto, Dwi Rizki Muhaimin Hasanudin Muhamad Kadapi Muhammad Afif Muhammad Rizki Muhammad, Fadlan Atalla Murgayanti Murgayanti Nadia Uliati Nguyen Quoc Khuong Nisa, Kahirun Noor Istifadah Noor Istifadah Nurizqia, Ghaida Marsha Nursuhud Nursuhud Osman, Mohd Azam Pakpahan, Hansel Arie PAUL BENYAMIN TIMOTIWU Pertiwi, Hanna Pipit Mutiara Pratita, Dian Galuh Pratomo, Adji Putra Perdana Prasetyo, Aditya Putri, Azizah Tiara Putri, Dina Putri, Envry Artanti Duidahayu RADEN AJENG DIANA WIDYASTUTI Rahma, Siti Auliya Rahmad, Khozaeni Bin Rahmat, Bayu Pradana Nur Ramadani, Selika Fitrian Reginawanti Hindersah Reza Maulana Rika Meliansyah Roedhy Poerwanto Roedhy Poerwanto Roedhy Poerwanto Rofiq, Muhamad Abdul Rossita Fauziah Ruminta Ruminta Saefur Rohman Saefur Rohman Samsuryadi Samsuryadi Sari, Stefina Liana Sarmayanta Sembiring Sastrika Anindita Semendawai, Jaka Naufal Setiawan, Deris Sidabutar, Alex Onesimus Siska Rasiska SIska Rasiska, SIska Sistyananda, Firstian Naufal Siti Julaeha, Siti Stefina Liana Sari Stefina Liana Sari Sucinta Aulida Khoerunnisa Susanto Susanto Syamsul Arifin, M. Agus Syariful Mubarok SYARIFUL MUBAROK SYARIFUL MUBAROK Syariful Mubarok Syariful Mubarok Tessa Zulenia Fitri Varinto, Irvan Waluyo, Nurmalita Wawan Sutari Wawan Sutari Wibawa, Rangga Widyastuti, R.A.D. Yanyan Mochamad Yani Yaya Sudarya Triana Yazid Idris, Mohd. Yudho Suprapto, Bhakti Yulianto, Fiky Yusti Yusti, Yusti Zulhipni Reno Saputra Elsi